The concept of semantic communication provides a novel approach for
applications in scenarios with limited communication resources. In this paper,
we propose an end-to-end (E2E) semantic molecular communication system, aiming
to enhance the efficiency of molecular communication systems by reducing the
transmitted information. Specifically, following the joint source channel
coding paradigm, the network is designed to encode the task-relevant
information into the concentration of the information molecules, which is
robust to the degradation of the molecular communication channel. Furthermore,
we propose a channel network to enable the E2E learning over the
non-differentiable molecular channel. Experimental results demonstrate the
superior performance of the semantic molecular communication system over the
conventional methods in classification tasks.

提出一种语义分子通信系统，通过减少传输的信息来增强分子通信系统的效率，并展示其在分类任务中相较于传统方法的卓越性能。

通过分子构建语义通信系统：一种端到端训练方法

Building Semantic Communication System via Molecules: An End-to-End  Training Approach

Molecular communication is a novel approach for data transmission between
miniaturized devices, especially in contexts where electrical signals are to be
avoided. The communication is based on sending molecules (or other particles)
at nano scale through channel instead sending electrons over a wire. Molecular
communication devices have a large potential in medical applications as they
offer an alternative to antenna-based transmission systems that may not be
applicable due to size, temperature, or radiation constraints. The
communication is achieved by transforming a digital signal into concentrations
of molecules. These molecules are then detected at the other end of the
communication channel and transformed back into a digital signal. Accurately
modeling the transmission channel is often not possible which may be due to a
lack of data or time-varying parameters of the channel (e. g., the movements of
a person wearing a medical device). This makes demodulation of the signal very
difficult. Many approaches for demodulation have been discussed with one
particular approach having tremendous success: artificial neural networks.
These networks imitate the decision process in the human brain and are capable
of reliably classifying noisy input data. Training such a network relies on a
large set of training data. As molecular communication as a technology is still
in its early development phase, this data is not always readily available. We
discuss neural network-based demodulation approaches relying on synthetic data
based on theoretical channel models as well as works using actual measurements
produced by a prototype test bed. In this work, we give a general overview over
the field molecular communication, discuss the challenges in the demodulations
process of transmitted signals, and present approaches to these challenges that
are based on artificial neural networks.

分子通信是一种在避免使用电信号的情况下，在微型设备之间进行数据传输的新方法。以纳米尺度通过信道发送分子（或其他粒子），而不是通过导线发送电子。我们讨论了基于人工神经网络的解调方法，这些网络模拟人脑的决策过程，并能可靠地对噪声干扰下的输入数据进行分类。

分子通信的人工智能

Artificial Intelligence for Molecular Communication

We consider detection based on deep learning, and show it is possible to
train detectors that perform well without any knowledge of the underlying
channel models. Moreover, when the channel model is known, we demonstrate that
it is possible to train detectors that do not require channel state information
(CSI). In particular, a technique we call a sliding bidirectional recurrent
neural network (SBRNN) is proposed for detection where, after training, the
detector estimates the data in real-time as the signal stream arrives at the
receiver. We evaluate this algorithm, as well as other neural network (NN)
architectures, using the Poisson channel model, which is applicable to both
optical and molecular communication systems. In addition, we also evaluate the
performance of this detection method applied to data sent over a molecular
communication platform, where the channel model is difficult to model
analytically. We show that SBRNN is computationally efficient, and can perform
detection under various channel conditions without knowing the underlying
channel model. We also demonstrate that the bit error rate (BER) performance of
the proposed SBRNN detector is better than that of a Viterbi detector with
imperfect CSI as well as that of other NN detectors that have been previously
proposed. Finally, we show that the SBRNN can perform well in rapidly changing
channels, where the coherence time is on the order of a single symbol duration.

该研究利用深度学习进行检测，通过提出一种双向递归神经网络（SBRNN）的技术，能够在不知道通道模型的情况下进行检测，且在分子通信系统中表现出良好的性能。

通信系统中数据序列的神经网络检测

Neural Network Detection of Data Sequences in Communication Systems

The design and analysis of communication systems typically rely on the
development of mathematical models that describe the underlying communication
channel, which dictates the relationship between the transmitted and the
received signals. However, in some systems, such as molecular communication
systems where chemical signals are used for transfer of information, it is not
possible to accurately model this relationship. In these scenarios, because of
the lack of mathematical channel models, a completely new approach to design
and analysis is required. In this work, we focus on one important aspect of
communication systems, the detection algorithms, and demonstrate that by
borrowing tools from deep learning, it is possible to train detectors that
perform well, without any knowledge of the underlying channel models. We
evaluate these algorithms using experimental data that is collected by a
chemical communication platform, where the channel model is unknown and
difficult to model analytically. We show that deep learning algorithms perform
significantly better than a simple detector that was used in previous works,
which also did not assume any knowledge of the channel.

本文介绍了一种新的通信系统设计和分析方法 —— 使用深度学习的检测算法在无法建立数学信道模型的分子通信系统中实现良好的检测性能，实验结果显示深度学习算法显著优于之前使用在无信道知识的情况下的简单检测器。